Creating Opportunities for Successful Artificial Intelligence Applications in the Upstream Petroleum Industry

2021 ◽  
Author(s):  
Fatai Adesina Anifowose
Keyword(s):  
Artificial Intelligence ◽  
Petroleum Industry ◽  
Digital Transformation ◽  
Reservoir Rock ◽  
Reservoir Properties ◽  
Business Goals ◽  
Exploration And Production ◽  
Domain Expertise ◽  
Number Of Publications ◽  
Core Description

Abstract The petroleum industry has continued to show more interest in the application of artificial intelligence (AI). Most professional gatherings now have sub-themes to highlight AI applications. Similarly, the number of publications featuring AI applications has increased. The industry is facing the challenge of scaling up the applications to practical and impactful levels. Most of the applications end up in technical publications and narrow proofs of concept. For the industry's digital transformation objective to be fully achieved, efforts are required to overcome the current limitations. This paper discusses possible causes of the prevailing challenges and prescribes a number of recommendations to overcome them. The recommendations include ways to handle data shortage and unavailability issues, and how AI projects can be designed to provide more impactful solutions, regenerate missing or incomplete logs, and provide alternative workflows to estimate certain reservoir properties. The results of three successful applications are presented to demonstrate the efficacy of the recommendations. The first application estimates a log of reservoir rock cementation factors from wireline data to overcome the limitation of the conventional approach of using a constant value. The second application used the machine learning methodology to regenerate missing logs possibly due to tool failure or bad hole conditions. The third application provides an alternative approach to estimate reservoir rock grain size to overcome the challenges of the conventional core description. Tips on how these applications can be integrated to create a bigger impact on exploration and production (E&P) workflows are shared. It is hoped that this paper will enrich the current AI implementation strategy and practice. It will also encourage increased synergy and collaborative integration of domain expertise and AI methods to make better impact and achieve the digital transformation of E&P business goals.

An Empirical Analysis of Artificial Intelligence, Big Data and Analytics Applications in Exploration and Production Operations

2021 ◽  
Author(s):  
Armstrong Lee Agbaji
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Empirical Analysis ◽  
Oil And Gas ◽  
Oil Industry ◽  
Digital Transformation ◽  
Deep Dive ◽  
Exploration And Production ◽  
Oil And Gas Operations

Abstract Oil and Gas operations are now being "datafied." Datafication in the oil industry refers to systematically extracting data from the various oilfield activities that are naturally occurring. Successful digital transformation hinges critically on an organization's ability to extract value from data. Extracting and analyzing data is getting harder as the volume, variety, and velocity of data continues to increase. Analytics can help us make better decisions, only if we can trust the integrity of the data going into the system. As digital technology continues to play a pivotal role in the oil industry, the role of reliable data and analytics has never been more consequential. This paper is an empirical analysis of how Artificial Intelligence (AI), big data and analytics has redefined oil and gas operations. It takes a deep dive into various AI and analytics technologies reshaping the industry, specifically as it relates to exploration and production operations, as well as other sectors of the industry. Several illustrative examples of transformative technologies reshaping the oil and gas value chain along with their innovative applications in real-time decision making are highlighted. It also describes the significant challenges that AI presents in the oil industry including algorithmic bias, cybersecurity, and trust. With digital transformation poised to re-invent the oil & gas industry, the paper also discusses energy transition, and makes some bold predictions about the oil industry of the future and the role of AI in that future. Big data lays the foundation for the broad adoption and application of artificial intelligence. Analytics and AI are going to be very powerful tools for making predictions with a precision that was previously impossible. Analysis of some of the AI and analytics tools studied shows that there is a huge gap between the people who use the data and the metadata. AI is as good as the ecosystem that supports it. Trusting AI and feeling confident with its decisions starts with trustworthy data. The data needs to be clean, accurate, devoid of bias, and protected. As the relationship between man and machine continues to evolve, and organizations continue to rely on data analytics to provide decision support services, it is imperative that we safeguard against making important technical and management decisions based on invalid or biased data and algorithm. The variegated outcomes observed from some of the AI and analytics tools studied in this research shows that, when it comes to adopting AI and analytics, the worm remains buried in the apple.

About digital transformation of the transport industry in Russia

2020 ◽  
Vol 19 (3) ◽  
pp. 95-105
Author(s):  
Natalia V. Vysotskaya ◽  
T. V. Kyrbatskaya
Keyword(s):  
Artificial Intelligence ◽  
Business Model ◽  
Data Science ◽  
Digital Transformation ◽  
Digital Culture ◽  
Blockchain Technology ◽  
Community Approach ◽  
Non Linear ◽  
Transport Industry ◽  
Artificial Intelligence Systems

The article is devoted to the consideration of the main directions of digital transformation of the transport industry in Russia. It is proposed in the process of digital transformation to integrate the community approach into the company's business model using blockchain technology and methods and results of data science; complement the new digital culture with a digital team and new communities that help management solve business problems; focus the attention of the company's management on its employees and develop those competencies in them that robots and artificial intelligence systems cannot implement: develop algorithmic, computable and non-linear thinking in all employees of the company.

scholarly journals The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

2021 ◽  
Author(s):  
Francesco Piccialli ◽  
Vincenzo Schiano di Cola ◽  
Fabio Giampaolo ◽  
Salvatore Cuomo
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trials ◽  
Digital Transformation ◽  
Daily Activities ◽  
Commercial Vehicles ◽  
Human Skills ◽  
To Come ◽  
Complex Scenario ◽  
The Impact

AbstractThe first few months of 2020 have profoundly changed the way we live our lives and carry out our daily activities. Although the widespread use of futuristic robotaxis and self-driving commercial vehicles has not yet become a reality, the COVID-19 pandemic has dramatically accelerated the adoption of Artificial Intelligence (AI) in different fields. We have witnessed the equivalent of two years of digital transformation compressed into just a few months. Whether it is in tracing epidemiological peaks or in transacting contactless payments, the impact of these developments has been almost immediate, and a window has opened up on what is to come. Here we analyze and discuss how AI can support us in facing the ongoing pandemic. Despite the numerous and undeniable contributions of AI, clinical trials and human skills are still required. Even if different strategies have been developed in different states worldwide, the fight against the pandemic seems to have found everywhere a valuable ally in AI, a global and open-source tool capable of providing assistance in this health emergency. A careful AI application would enable us to operate within this complex scenario involving healthcare, society and research.

Big Data Technology, Evolving Knowledge Skills and Emerging Roles

2016 ◽  
Vol 16 (4) ◽  
pp. 219-224 ◽  
Author(s):  
Alex Smith
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Emerging Technology ◽  
Law Firms ◽  
Support Functions ◽  
Legal Problems ◽  
Artificial Intelligence Technology ◽  
Domain Expertise ◽  
New Generation ◽  
Big Data Technology

AbstractIn a world where articles and tweets are discussing how artificial intelligence technology will replace humans, including lawyers and their support functions in firms, it can be hard to understand what the future holds. This article, written by Alex Smith, is based on his presentation at the British and Irish Association of Law Librarians conference in Dublin 2016 and looks at demystifying the emerging technology boom and identifies the expertise needed to make these tools work and be deployed in law firms. The article then looks at the skills and expertise of the knowledge and information teams, based in law firms, and suggests how they are ideally placed to lead these challenges as a result of their domain expertise and their existing, well defined skills that are essential to this new generation of technology. The article looks at the new technical environment, the emerging areas of products and legal problems, the skills needed for the new roles that this revolution is creating and how this could fit into a reimagined knowledge team.

Das Zeitalter der Künstlichen Intelligenz als Herausforderung für die Parlamente

2021 ◽  
Vol 52 (1) ◽  
pp. 159-181
Author(s):  
Arne Pilniok
Keyword(s):  
Artificial Intelligence ◽  
Digital Technologies ◽  
Digital Transformation ◽  
Processes Of Change ◽  
Parliamentary Democracy ◽  
Democratic Representation ◽  
Government Administration ◽  
Technological Developments ◽  
Different Dimensions ◽  
The Government

The digital transformation is permanently changing the government, administration, and society . This process is being intensified by the much-discussed technologies of artificial intelligence, and poses a variety of challenges for parliaments and indirectly for parliamen­tary studies . Their different dimensions have not been discussed comprehensively so far, although the technological developments affect all parliamentary functions and their prem­ises . This article systematizes and structures the various effects of the age of artificial intel­ligence on parliamentary democracy . Namely, the conditions of democratic representation change, the innovation-friendly regulation of digital technologies becomes a parliamentary task, parliamentary control has to be adjusted to the use of algorithms and artificial intelli­gence in government and administration, and possibly, the epistemological and organiza­tional structures of parliamentary work might have to be adapted . This provides starting points for future detailed analyses to adequately capture these processes of change and to accompany them from different disciplinary perspectives .

Using AI/ML to Explore & Develop Quickly and Efficiently

2021 ◽  
Author(s):  
Anthony Aming
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Seismic Data ◽  
3D Seismic ◽  
Structure Amplitude ◽  
Zero Crossing ◽  
Petroleum Systems ◽  
Exploration And Production ◽  
Data Volume ◽  
3D Seismic Data

Abstract See how application of a fully trained Artificial Intelligence (AI) / Machine Learning (ML) technology applied to 3D seismic data volumes delivers an unbiased data driven assessment of entire volumes or corporate seismic data libraries quickly. Whether the analysis is undertaken using onsite hardware or a cloud based mega cluster, this automated approach provides unparalleled insights for the interpretation and prospectivity analysis of any dataset. The Artificial Intelligence (AI) / Machine Learning (ML) technology uses unsupervised genetics algorithms to create families of waveforms, called GeoPopulations, that are used to derive Amplitude, Structure (time or depth depending on the input 3D seismic volume) and the new seismic Fitness attribute. We will show how Fitness is used to interpret paleo geomorphology and facies maps for every peak, trough and zero crossing of the 3D seismic volume. Using the Structure, Amplitude and Fitness attribute maps created for every peak, trough and zero crossing the Exploration and Production (E&P) team can evaluate and mitigate Geological and Geophysical (G&G) risks and uncertainty associated with their petroleum systems quickly using the entire 3D seismic data volume.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

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